Face tracking is the process of determining the movement trajectory of a target face based on face identification. Face identification generally includes two parts: the first is to conduct face detection with respect to the image, and the second is to conduct particular face judging with respect to the detected face region. In other words, first, a face is detected in an inputted image, and then the detected face is compared with samples in a face database to identify the target face. After the target face is identified, how to conduct effective tracking of the movement trajectory of the face is a problem to be solved in face tracking.
In the prior art, Camshift (Continuously Adaptive Mean Shift) algorithm based on skin color feature is extensively used in face tracking. The Camshift algorithm is an improvement of MeanShift algorithm, and its basic idea is to conduct MeanShift calculation with respect to all frames of a video image, use the calculating result of the preceding frame (namely, the center and size of the searching window) as the initial value of the searching window of the next frame of MeanShift algorithm, and repeat the iteration. The Camshift algorithm can self-adaptively adjust the position and size of the searching window according to the tracking result of the current frame and realize real-time face tracking.
However, the Camshift algorithm uses the skin color model to track, relies heavily on factors such as the environment and the background, has a poor anti-interference ability and low tracking accuracy.